An ai-enabled supercritical foam production anomaly monitoring management system

By constructing a production segment complaint risk mapping model and a dynamic risk tolerance threshold range, the problem that the implicit risks of production events cannot be quantified and transmitted to order allocation decisions in existing technologies has been solved, and the differentiated allocation of finished products outbound and matching of customer needs have been realized.

CN122155126APending Publication Date: 2026-06-05FUJIAN XINRUI NEW MATERIALS TECHNOLOGY CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
FUJIAN XINRUI NEW MATERIALS TECHNOLOGY CO LTD
Filing Date
2026-05-08
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing technologies fail to establish a mapping relationship between non-parametric production event sequences and customer complaint risks, and cannot distinguish the risk tendencies of finished products by production segments and match the complaint tolerance of different customers, resulting in a disconnect between finished product outbound allocation decisions and quality risks.

Method used

The non-parametric production event log file is input through the log input module. The event type coding sequence is extracted, and a production segment complaint risk mapping model is constructed by hierarchical clustering using a weighted edit distance algorithm. The frequency distribution vector of customer complaint events is extracted based on the time decay weighting method. A dynamic risk tolerance threshold range is set to realize the binding of risk tendency coding with customer allocation.

Benefits of technology

It enables end-to-end data flow from production event sequence to customer complaint risk, solving the problem that the implicit risks of production events cannot be quantified and transmitted to the order allocation decision-making stage, and ensuring that the differentiated allocation of finished products is matched with customer needs.

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Abstract

The application discloses an AI-enabled supercritical foaming material production anomaly monitoring and management system, and belongs to the technical field of production management, and specifically comprises: a log input module, which inputs a non-parametric production event log file; a vector extraction module, which obtains a customer code list and extracts a complaint event frequency distribution vector; a segment coding module, which divides production events into multiple production segment intervals and extracts an event type coding sequence; a risk mapping module, which inputs the coding sequence into a complaint risk mapping model to output complaint risk tendency coding; a matching and distribution module, which inputs the complaint vector and the risk coding into a distribution rule library to output a distribution binding relationship record; and an outbound identification module, which stores a finished product packaging box identification code in association with a customer code and adds a risk code in a delivery document. The application realizes the mapping between production event sequences and customer complaint risks and the differentiated distribution of finished products.
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